Data structure

## Observations: 1,704
## Variables: 6
## $ country   <fct> Afghanistan, Afghanistan, Afghanistan, Afghanistan, ...
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia...
## $ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992...
## $ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.8...
## $ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 1488...
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 78...

## Skim summary statistics
##  n obs: 1704 
##  n variables: 6 
## 
## -- Variable type:factor ----------------------------------------------------------------------------
##   variable missing complete    n n_unique
##  continent       0     1704 1704        5
##    country       0     1704 1704      142
##                              top_counts ordered
##  Afr: 624, Asi: 396, Eur: 360, Ame: 300   FALSE
##      Afg: 12, Alb: 12, Alg: 12, Ang: 12   FALSE
## 
## -- Variable type:integer ---------------------------------------------------------------------------
##  variable missing complete    n    mean       sd    p0        p25     p50
##       pop       0     1704 1704 3e+07    1.1e+08 60011 2793664    7e+06  
##      year       0     1704 1704  1979.5 17.27     1952    1965.75  1979.5
##       p75       p100     hist
##  2e+07       1.3e+09 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
##   1993.25 2007       <U+2587><U+2583><U+2587><U+2583><U+2583><U+2587><U+2583><U+2587>
## 
## -- Variable type:numeric ---------------------------------------------------------------------------
##   variable missing complete    n    mean      sd     p0     p25     p50
##  gdpPercap       0     1704 1704 7215.33 9857.45 241.17 1202.06 3531.85
##    lifeExp       0     1704 1704   59.47   12.92  23.6    48.2    60.71
##      p75      p100     hist
##  9325.46 113523.13 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
##    70.85     82.6  <U+2581><U+2582><U+2585><U+2585><U+2585><U+2585><U+2587><U+2583>

Filtering the gapminder data for Canada

Pseudo code 1. Take the gapminder data AND THEN 2. Filter out all the data except for Canada

R code

## # A tibble: 12 x 6
##    country continent  year lifeExp      pop gdpPercap
##    <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Canada  Americas   1952    68.8 14785584    11367.
##  2 Canada  Americas   1957    70.0 17010154    12490.
##  3 Canada  Americas   1962    71.3 18985849    13462.
##  4 Canada  Americas   1967    72.1 20819767    16077.
##  5 Canada  Americas   1972    72.9 22284500    18971.
##  6 Canada  Americas   1977    74.2 23796400    22091.
##  7 Canada  Americas   1982    75.8 25201900    22899.
##  8 Canada  Americas   1987    76.9 26549700    26627.
##  9 Canada  Americas   1992    78.0 28523502    26343.
## 10 Canada  Americas   1997    78.6 30305843    28955.
## 11 Canada  Americas   2002    79.8 31902268    33329.
## 12 Canada  Americas   2007    80.7 33390141    36319.

Plotting population over years

data

data and aesthetics

data, aesthetics and geometric object

YOUR TURN: Create a new data set for your favorite country and plot its population over years

Color

Size

Shape (Not great for > 3 categories)

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.

## Warning: Removed 6 rows containing missing values (geom_point).

What if you use all?

Line

Histogram

Now using the compete gapminder data:

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Boxplot

Creating a scatter plot for all countries

Now we’ll use the complete gapminder data

YOUR TURN: Copy the above code and paste it below. Replace year with gdpPercap

Colour the continents

YOUR TURN: Copy the above code and paste in the R chunk below. Then change colorto size and run it.

Separate the continents using facets

Change scales

Can we create a facet for each country? > YES

YOUR TURN: Create a facted plot for each year. Use x = gdpPercap, y = lifeExp and color = continent. You can use the above code to start.

Original

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Transformed

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Let’s try 1 continent (Americas)

## [1] Asia     Europe   Africa   Americas Oceania 
## Levels: Africa Americas Asia Europe Oceania

Create a dataframe for Americas

Plot

YOUR TURN: Do a similar plot like above for Oceania

YOUR TURN: Using the 2007 data set (created below), plot the life expectancy as a function of GDP. Color each continent and also use size = pop.

Data

Plot

plot everything

increase point transparency

add color

transform

facet by year

improve point size

labels

## # A tibble: 142 x 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Albania     Europe     1952    55.2  1282697     1601.
##  3 Algeria     Africa     1952    43.1  9279525     2449.
##  4 Angola      Africa     1952    30.0  4232095     3521.
##  5 Argentina   Americas   1952    62.5 17876956     5911.
##  6 Australia   Oceania    1952    69.1  8691212    10040.
##  7 Austria     Europe     1952    66.8  6927772     6137.
##  8 Bahrain     Asia       1952    50.9   120447     9867.
##  9 Bangladesh  Asia       1952    37.5 46886859      684.
## 10 Belgium     Europe     1952    68    8730405     8343.
## # ... with 132 more rows

create data for labels

add axis labels and title

default themes

save plot